The second part of the methodology is the Convolutional Neural Network (CNN) for training and testing. To this aim, the first step is to build a database following the procedure of the sample generation. The data is divided into two sets of training and testing. The selected architecture for CNN in this paper is AlexNet [1]} due to its excellent ability to extract local and multilevel features in different applications, and its successful implementation in vehicle classification in earlier work [2]}. AlexNet consists of eight layers, where the first five are convolutional layers, (some of them followed by max-pooling layers), and the last three are fully connected (FC) layers, as shown in Figure REF . AlexNet requires a vast database for its training; for this reason, in [2]} a Transfer Learning method [4]}, [5]} was proposed to reduce the size of the required databases. In this method, an alternative data is used to train lower-level layers, which are responsible for extracting the generic features of the input data. While, the real data is used to train the last layers, which carry out the classification task. Finally, the trained model accuracy is estimated using the validation dataset.
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